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Record W1976742789 · doi:10.1139/f07-194

Influence of seasonal changes in runoff and extreme events on dissolved organic carbon trends in wetland- and upland-draining streams

2008· article· en· W1976742789 on OpenAlexfundvenueaboutno aff
M. Catherine Eimers, Jim Buttle, Shaun A. Watmough

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolved organic carbonEnvironmental scienceWetlandHydrology (agriculture)TributarySTREAMSSurface runoffSpring (device)Dominance (genetics)PeatStreamflowDrainage basinSeasonalityOceanographyEcologyGeologyChemistryGeography

Abstract

fetched live from OpenAlex

Dissolved organic carbon (DOC) fluxes at eight headwater basins in south-central Ontario were strongly related to seasonal streamflow, and extreme events contributed to both interannual and intercatchment variability. Six catchments with high stream DOC and greater peatland coverage exhibited a different seasonal pattern of DOC concentration compared with two catchments with low DOC and less wetland influence. In wetland-dominated catchments, DOC concentrations decreased during fall wet-up and spring melt, and because of the dominance of the spring melt period in annual budgets, variations in spring flow explained 39%–48% of the intervariability in DOC concentration. Significant increases in average DOC concentration between 1980 and 2001 at all six wetland-dominated catchments were driven by relatively high DOC concentrations in the latter years of record, consistent with low spring flow in these years, and were not translated into greater DOC export to downstream lakes. Localized rainstorms in summer and fall resulted in differences in DOC export among adjacent catchments, and a single fall storm in September 1998 was only detected at one of six catchments draining into Harp Lake but accounted for one-quarter of the annual tributary DOC load to the lake.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.201
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations131
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicSoil and Water Nutrient DynamicsFrench-language works237,207